Diffusion alignment adapts pretrained diffusion models to sample from reward-tilted distributions along the denoising trajectory. This process naturally admits a Sequential Monte Carlo (SMC) interpretation, where the denoising model acts as a proposal and reward guidance induces importance weights. Motivated by this view, we introduce Variance Minimisation Policy Optimisation (VMPO), which formulates diffusion alignment as minimising the variance of log importance weights rather than directly optimising a Kullback-Leibler (KL) based objective. We prove that the variance objective is minimised by the reward-tilted target distribution and that, under on-policy sampling, its gradient coincides with that of standard KL-based alignment. This perspective offers a common lens for understanding diffusion alignment. Under different choices of potential functions and variance minimisation strategies, VMPO recovers various existing methods, while also suggesting new design directions beyond KL.
@article{arxiv.2602.12229,
title = {Diffusion Alignment Beyond KL: Variance Minimisation as Effective Policy Optimiser},
author = {Zijing Ou and Jacob Si and Junyi Zhu and Ondrej Bohdal and Mete Ozay and Taha Ceritli and Yingzhen Li},
journal= {arXiv preprint arXiv:2602.12229},
year = {2026}
}